It is often difficult to obtain the necessary amount of training data when it originates from physical experiments. This can be due to multiple factors, such as high costs, risks & difficulties associated with the experiments, or data privacy clauses.
There are two possible approaches to deal with the scarcity of experimental data. The first approach relies on enhancing the DL model with the information on underlying physics – an approach popularly termed as Physics Informed Neural Networks (PINN) [1]}, [2]}, [3]}, [4]}. The second approach includes the underlying physics implicitly, through high-fidelity simulations done in silico to provide the necessary amount of synthetically generated data, which has shown to be useful in various applications [5]}, [6]}, [7]}, [8]}, [9]}. In this work, we will follow the latter approach and will focus on DL surrogate models that are trained on synthetically generated data from finite element simulations in non-linear elasticity.
